Neural Noiseprint Transfer: A Generic Noiseprint-Based Counter Forensics Framework

License

Copyright (c) 2022 Ahmed Elliethy.

All rights reserved.

This software should be used, reproduced and modified only for informational and nonprofit purposes.

By downloading and/or using any of these files, you implicitly agree to all the terms of the license, as specified in the document LICENSE.txt (included in this package)

Installation

The code requires Python 3.x and PyTorch 1.12.

To install Python 3.x for Ubuntu, you can run:

apt-get update
apt-get install -y python3.8 python3.8-dev python3-pip python3-venv

To install PyTorch, follow the link here https://pytorch.org

Usage

To extract the noiseprint, run:

python3 main_nnt.py --img_forged_filename=<input forged image> --img_auth_filename=<input authentic image> --out_filename=<output file> --method_name=<method that can be 'injection' or 'optimization' (default='injection')> --visualize_results=<True if want to display results (default=true)>

Demo

To execute a demo, run the following

python3 main_nnt.py --img_forged_filename='Demo/splicing-01.png' --img_auth_filename='Demo/normal-01.png' --out_filename='output.png'

Output

PSNR = 34.12433053884067, and SSIM = 0.939936182563455

python3 main_nnt.py --img_forged_filename='Demo/splicing-02.png' --img_auth_filename='Demo/normal-02.png' --out_filename='output.png'

Output

PSNR = 31.57684118074469, and SSIM = 0.8961242284468497

Reference

Elliethy A. 2023. Neural noiseprint transfer: a generic noiseprint-based counter forensics framework. PeerJ Computer Science 9:e1359 https://doi.org/10.7717/peerj-cs.1359

@article{Elliethy2022_NNT,
  title={Neural Noiseprint Transfer: A Generic Noiseprint-Based Counter Forensics Framework},
  author={Ahmed Elliethy},
  journal={PeerJ Computer Science},
  doi={https://doi.org/10.7717/peerj-cs.1359}
}